Abstract
Decision-making algorithms are becoming intertwined with each aspect of society. As we automate tasks which result in outcomes that affect an individual’s life, the need for assessing and understanding the ethical consequences of these processes becomes vital. With bias often originating from the datasets imbalanced group distributions, we propose a novel approach to in-processing fairness techniques, by considering training at a group-level. Adapting the standard training process of the logistic regression, our approach considers aggregating coefficient derivatives at a group-level to produce fairer outcomes. We demonstrate on two real-world datasets that our approach provides groups with more equal weighting towards defining the model parameters and displays potential to reduce unfairness disparities in group imbalanced data. Our experimental results illustrate a stronger influence on improving fairness when considering binary sensitive attributes, which may prove beneficial in continuing to construct fair algorithms to reduce biases existing in decision-making practices. Whilst the results present our group-level approach achieving less fair results than current state-of-the-art directly optimized fairness techniques, we primarily observe improved fairness over fairness-agnostic models. Subsequently, we find our novel approach towards fair algorithms to be a small but crucial step towards developing new methods for fair decision-making algorithms.
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Elliott, M., P., D. (2023). A Group-Level Learning Approach Using Logistic Regression for Fairer Decisions. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_25
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